全景图像驱动的点云初始化用于3D重建
Haoyu Qian1,2, Lidong Yang1,2, Jing Wang3
1School of Digital and Intelligent Industry, Inner Mongolia University of Science and Technology, Baotou 014010, China.
Sensors (Basel, Switzerland)
|November 27, 2025
概括
本研究介绍了一种新的以文本为导向的3D场景生成管道,使用全景图像和3D高斯斯点击. 它克服了改善虚拟现实和数字双胞胎应用程序的初始化挑战.
科学领域:
- 计算机视觉 计算机视觉
- 计算机图形 计算机图形
- 人工智能的人工智能
背景情况:
- 微分染具有先进的静态3D场景重建.
- 现有的方法通常依赖于密集的多视图图像,这是很难获得的.
- 不够的初始化仍然是3D场景重建的一个重大挑战.
研究的目的:
- 提出一个基于文本的3D场景生成管道,解决初始化问题.
- 提高3D场景重建的质量和效率.
- 为了减少对密集多视图图像的依赖.
主要方法:
- 使用全景图像作为中间表示.
- 整合了3D高斯斯裂纹,以改善重建.
- 实现用于点云初始化的斐波那契格子采样.
- 采用密集的视角伪标签策略,用于教师-学生蒸.
主要成果:
- 实现了更准确的场景几何和强大的特征学习.
- 与最先进的方法相比,证明了卓越的性能.
- 在没有明确的多视图地面真相的情况下,通过标准重建指标验证的有效性.
结论:
- 拟议的管道有效地解决了3D场景重建中的初始化挑战.
- 该方法为基于文本的3D场景生成提供了一个有前途的方法.
- 这项工作通过提高重建能力来推进虚拟现实和数字双胞胎技术.
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